Let’s imagine for a moment that you’ve arrived at a great hotel you picked up after comparing prices on your favorite travel app. As you drop off your luggage, the app proactively suggests a few perfect spots to eat nearby. It knows it’s getting close to lunchtime and that you love sushi. You finish your spicy tuna roll and pay for the meal by tapping your phone screen. The app quickly notifies you about impending rain storm and suggests that you switch that bike tour you were planning to a top-rated portrait gallery that had caught your eye the day before—a good way to have fun and stay dry. “Would you like to confirm your skip-the-line tour? You’ll get 30% off if you book now,” it says in a soothing voice.

All of this is theoretically possible in the not-too-distant future through the power of machine learning, an application of artificial intelligence that can be used to offer more personalized and contextual recommendations.

These days, machine learning is in its infancy and has only just begun to transform the consumer experience in a wide range of industries from video streaming to ecommerce. Ever received a movie recommendation from Netflix because it’s like other movies you’ve watched? That’s machine learning at work. Has Amazon ever told you that people who bought one product also tend to buy some other product? Machine learning.

While there is a ton of opportunity for its application in travel, there are also a lot of unique challenges ahead that must be addressed in order to succeed in offering consumers more delightful pre-trip and in-destination experiences. If your streaming video service recommends a bad movie, then the viewer has lost, at worst, a couple hours of time. But, if a travel site recommends the wrong place to stay or things to do, then the opportunity cost is potentially much greater when you consider the wasted time and money. So, it’s imperative to delight consumers with the right recommendations for every trip.

3 Machine Learning Lessons in Travel

At TripAdvisor, we kicked off our work in machine learning in 2014 with our “Just For You” feature, which ranked hotels based on each user’s personal preferences, shopping behavior and past reviews. We started building a deep set of experience in the field, and through that effort, we learned a lot about the way travelers plan and book their trips:

Lesson #1: Travel Personas Vary – The process of making contextual recommendations in travel is more challenging than in many other industries. That’s because a person’s travel persona can change quite a bit depending on the type of trip they take and with whom they are traveling. Think of how differently you travel during a business trip, as compared to a romantic trip with your partner or with kids. That cozy boutique B&B that’s perfect for one type of getaway might be a complete disaster for the next. For this reason, we’ve worked to better understand how to adapt recommendations based on the context of a person’s trip. This insight has been key to our efforts to create better, more intuitive and personalized experiences for our community of travelers.

Lesson #2: Recommendations are Best When We Consider Both Implicit and Explicit Signals – Divining recommendations from past behavior and the behavior of like-minded people alone is not enough. Consumers want and need to be able to explicitly provide even stronger signals about what kind of trip or experience they want or change their preferences as needed. For that reason, it’s important to give travelers the ability to filter and refine personalized recommendations based on machine learning.

Lesson #3: Personalized Recommendations Benefit Travelers and Businesses Alike – We also learned that 1-N ranking of top rated hotels is not always most helpful to travelers, as the highest ranked properties in a given destination may not always align to a particular traveler’s needs or search queries. For example, the best hotel in the city might break the traveler’s bank or lack availability for the dates searched. Machine learning has helped us offer smarter sorting tailored to the needs of each user. The end result is better recommendations for traveler and better leads for hotels.

How TripAdvisor Has Applied These Lessons

Through a fail-fast approach, we’ve taken all these learnings into account as we continue to perfect our use of machine learning. Ironically, to make perfect recommendations for the individual, you need a virtual treasure trove of aggregated, anonymized data from millions of other consumers.

Thankfully, we have the benefit of 15+ years of data, including 500 million reviews and opinions from travelers all over the world, and our 400 million monthly users have helped us refine tags and other attributes about businesses. By distilling these semantic signals, we offer up more individualized recommendations and help increasingly mobile travelers book the best restaurants, attractions and hotels for their trip. We’re also able to send real-time, location-based recommendations via their mobile devices.

Most recently, we have applied machine learning to our new “Best Value” sort, which helps travelers get the best bang for their buck by sorting hotels based on a combination of factors, including traveler ratings, hotel rates, booking popularity, brand affinity and location. This new sort adapts to the consumer’s preferences, context and previous behavior on our app or site.

We’ve also baked machine learning into new “Things to Do” and “Restaurants” categories to offer more personalized and contextual recommendations. Let’s say it’s early in the morning and you’re exploring a new city, we now intuitively recommend a great breakfast spot nearby. That advantage is that you no longer need to proactively perform that query—it’s already done for you. Or, if you’re looking for a great hamburger, we don’t just suggest spots that happen to have burgers; we recommend the best restaurants for burgers. A small but important distinction. It’s the difference between a French restaurant and a place that serves French bread, and machine learning is helping to make those distinctions in new and exciting ways.

The Future of Travel

Machine learning is great but there is a lot of hype around it. I’ve been hearing a lot of grandiose ideas about what the technology could become in the future that, in my opinion, are a bit far-fetched or out of touch with how real consumers behave. For example, it’s unlikely that many would be willing to talk to a chatbot on their device, ask for hotel recommendations and book a room sight unseen. The visual aspect of trip planning remains critical to the process.

So, what does the future hold for travel? As we imagine a world in which we begin to solve the big machine learning challenges and perfect the technology, I predict that travel will increasingly become more:

Personalized – Travel recommendations will be increasingly tailored to the individual

Contextual – Suggestions about where to go and what to do will be based ever more on what type of trip you’re on, where you are, the time of day and the weather outside

Automated – The amount of input a user needs to provide will gradually decline

Assistive – Travel recommendations will be increasingly based on passive information based on proactive queries

Comprehensive – Every aspect of the trip and ancillary services will be combined in unique and super helpful ways

As the application of machine learning evolves, the technology will recede into the background and the process of planning and booking a trip will become even more delightful. We’re at the beginning of what is bound to be a very exciting journey in travel.

Adam Medros heads worldwide product development for TripAdvisor. Since joining TripAdvisor in 2004, Adam has overseen improvements to core functionality, content operations, international expansion, and development of a wide variety of travel planning features including the TripAdvisor and City Guides mobile apps on iOS and Android, the “Trip Friends” social features, and Facebook Instant Personalization on TripAdvisor. Prior to joining TripAdvisor, Adam held Senior Analyst and Product Manager positions at Nordstrom.com, Amazon.com, and The Parthenon Group. He serves on the Board of Directors of Wordstream and the Massachusetts Innovation & Technology Exchange (MITX) and was previously a board member of Oneforty.com and an adviser to Care.com. Adam holds an MBA from Harvard Business School and a bachelor’s degree from Dartmouth College.